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Abstract:

Disclosed is an approach for allowing a business entity to access and
integrate with social media data, which is capable of accessing data
across multiple types of internet-based sources of social data and
commentary and to perform semantic analysis upon that data. Tags are
associated with the analyzed content that pertain to actionable
categorization of the data. Community managers can then view and take
action with respect to the data. In addition, enterprise business
applications can utilize the tagged data to perform business processing.

Claims:

1. A method implemented with a processor, comprising: receiving content
from one or more sources; performing semantic analysis, classification,
and tagging on the content received from the social media sources; and
generating actionable data for an enterprise application based upon
results of performing semantic analysis, classification, and tagging on
the content received from the sources.

2. The method of claim 1, in which the semantic analysis comprises latent
semantic analysis (LSA).

3. The method of claim 1, in which the semantic analysis identifies
themes within the content.

5. The method of claim 1, in which the social data sources include at
least one of a social network, blog or web feed.

6. The method of claim 5, in which the social data sources comprise
internal social data network data or internal company data sources.

7. The method of claim 1, in which rich-content tags are associated with
the content to create the actionable data.

8. The method of claim 7, in which messages are dispatched to the
enterprise application based at least in part upon the tags.

9. The method of claim 8, in which the enterprise application uses the
messages to perform business processing.

10. The method of claim 8, in which rules are used to process the
messages.

11. The method of claim 8, in which the tags correspond to areas of
analytical importance with respect to the organizations that will be
consuming the results of the analysis.

12. The method of claim 7, in which the tags are used to dispatch
messages to vertical applications.

13. The method of claim 1, in which non-social data is analyzed.

14. The method of claim 1, in which semantic filtering is performed.

15. The method of claim 1, in which social profile data is analyzed.

16. A tangible computer readable medium having stored thereon a sequence
of instructions which, when executed by a processor causes the processor
to execute a process comprising: receiving content from one or more
sources; performing semantic analysis, classification, and tagging on the
content received from the social media sources; and generating actionable
data for an enterprise application based upon results of performing
semantic analysis, classification, and tagging on the content received
from the sources.

18. The computer readable medium of claim 16, in which the semantic
analysis identifies themes within the content.

19. The computer readable medium of claim 16, in which the social data
sources include at least one of a social network, blog or web feed.

20. The computer readable medium of claim 16, in which tags are
associated with the content to create the actionable data.

21. The computer readable medium of claim 20, in which messages are
dispatched to the enterprise application based at least in part upon the
tags.

22. The computer readable medium of claim 21, in which the enterprise
application uses the messages to perform business processing.

23. The computer readable medium of claim 21, in which rules are used to
process the messages.

24. The computer readable medium of claim 20, in which the tags are used
to dispatch messages to vertical applications.

25. The computer readable medium of claim 16, in which non-social data is
analyzed.

26. The computer readable medium of claim 16, in which semantic filtering
is performed.

27. The computer readable medium of claim 16, in which social profile
data is analyzed.

28. A system, comprising: a processor; a memory comprising computer code
executed using the processor, in which the computer code implements a
process comprising receiving content from one or more sources, performing
semantic analysis, classification, and tagging on the content received
from the social media sources, and generating actionable data for an
enterprise application based upon results of performing semantic
analysis, classification, and tagging on the content received from the
sources.

29. The system of claim 28, further comprising a dashboard tool to
visualize results from performing the semantic analysis, classification,
and tagging.

30. The system of claim 28, in which the semantic analysis comprises
latent semantic analysis (LSA).

31. The system of claim 28, in which the semantic analysis identifies
themes within the content.

32. The system of claim 28, in which the social data sources include at
least one of a social network, blog or web feed.

33. The system of claim 28, in which tags are associated with the content
to create the actionable data.

34. The system of claim 33, in which messages are dispatched to the
enterprise application based at least in part upon the tags.

35. The system of claim 34, in which the enterprise application uses the
messages to perform business processing.

36. The system of claim 34, in which rules are used to process the
messages.

37. The system of claim 33, in which the tags are used to dispatch
messages to vertical applications.

[0002] Given the widespread availability and usage of the internet by
consumers, many businesses have become interested in being able to
effectively monitor the content and commentary provided by such
consumers. Interactive websites such as social networks and blogs provide
a wealth of useful information that can be advantageously used by a
business.

[0003] It would be very useful to provide an effective mechanism to allow
businesses and enterprise business applications to interact with and take
action upon data that originate from online sources of social data and
commentary. For example, consider a CRM (Customer Relationship
Management) application, which is designed to facilitate the ability of a
business to create, develop, and build relationships with its customers
or potential customers. It would be very desirable to allow the business
CRM application to stay informed of actionable social networking content,
for example, to identify potential customers and possible sales leads.

[0004] As another example, brand managers are often interested in
monitoring commentary on the internet regarding their brands or
competitors' brands. Brand managers may read the commentary to gauge
interest in their marketing materials, receive feedback regarding their
products, or take further action regarding any inflammatory postings.

[0005] Embodiments of the present invention provide a system, method, and
computer program product for allowing a business entity to access and
integrate with social media data. The invention is capable of accessing
data across multiple types of internet-based sources of social data and
commentary and to perform semantic analysis upon that data. Tags are
associated with the analyzed content that pertain to actionable
categorization of the data. Community managers can then view and take
action with respect to the data. In addition, enterprise business
applications can utilize the tagged data to perform business processing.

[0006] Other additional objects, features, and advantages of the invention
are described in the detailed description, figures, and claims.

BRIEF DESCRIPTION OF FIGURES

[0007]FIG. 1 illustrates an example system which may be employed in some
embodiments of the invention to implement analysis and integration of
social media data with enterprises and enterprise applications.

[0008]FIG. 2 shows an architecture for implementing a social media
marketing and engagement framework according to some embodiments of the
invention.

[0009]FIG. 3 shows a table of example types of information that may be
part of an actionable social message.

[0010] FIGS. 4A and 4B show flowcharts of approaches to implement some
embodiments of the invention.

[0011]FIG. 5 illustrates an example system which may be employed in some
embodiments of the invention to implement analysis of an internal social
network.

[0012]FIG. 6 shows a flowchart of an approach to implement some
embodiments of the invention.

[0013]FIG. 7 shows an architecture of an example computing system with
which the invention may be implemented.

DETAILED DESCRIPTION

[0014] The present disclosure is directed to an approach for allowing a
business entity to access and integrate with social media data. The
invention is capable of accessing data across multiple types of
internet-based sources of social data and commentary and to perform
semantic analysis upon that data. Tags are associated with the analyzed
content that pertain to actionable categorization of the data. Users can
then view and take action with respect to the data. In addition,
enterprise business applications can utilize the tagged data to perform
business processing.

[0015]FIG. 1 illustrates an example system 100 which may be employed in
some embodiments of the invention to implement analysis and integration
of social media data with enterprises and enterprise applications. The
system 100 includes one or more users at one or more user stations 102
that use the system 100 to operate the enterprise application 106 and the
social network data analysis and integration application 108. The user
station 102 comprises any type of computing station that may be used to
operate or interface with the applications 106/108 in the system 100.
Examples of such user stations include, for example, workstations,
personal computers, or remote computing terminals. The user station 102
comprises a display device, such as a display monitor, for displaying a
user interface to users at the user station. The user station 102 also
comprises one or more input devices for the user to provide operational
control over the activities of the system 100, such as a mouse or
keyboard to manipulate a pointing object in a graphical user interface to
generate user inputs to the enterprise application and/or social
networking application.

[0016] According to some embodiments, integration is provided between an
enterprise application 106 and a social networking application. For the
purposes of explanation, one or more embodiments are illustratively
described with reference to CRM applications as enterprise application
106. It is noted, however, that the invention may be applied to other
types of enterprise applications as well, and is not to be limited to CRM
applications unless explicitly claimed as such.

[0017] The enterprise application 106 comprises any business-related
application that provides visibility and control over various aspects of
a business. Such enterprise/business applications can include, without
limitation, customer relations management ("CRM") applications,
enterprise resource planning ("ERP") applications, supply chain
management applications, and other applications dealing with various
finance, accounting, manufacturing, human resources, and/or distribution
functions, to name but a few examples. Exemplary enterprise application
suites include, without limitation, Oracle Fusion, Oracle eBusiness
Suite, JD Edwards Enterprise One, Oracle Cloud, PeopleSoft, and Siebel
all of which are available from Oracle Corporation of Redwood Shores,
Calif.

[0018] The social data integration and analysis application 108 provides a
framework for performing social media marketing and engagement
activities. The social data integration and analysis application 106
receives data from one or more online social data sources 104. Such
social data sources include, for example, websites such as a social
network or blog or web feed (e.g., Facebook, Twitter, Blogger, and RSS).
The content may include one or more comments (e.g., Facebook comment,
comment to a blog post, reply to a previous comment) or uploaded postings
(e.g., images and associated metadata, text, rich media, URLs) at one or
more sources. The social data/content may therefore comprise a variety of
forms and/or types.

[0019] Semantic filtering and analysis is performed upon the social data.
Based upon this analysis, rich-content tags are associated with the
content to create actionable social data 112. The actionable social data
112 is used by any users (such as community managers), e.g., to view and
respond to messages. In addition, messages can be dispatched to the
enterprise application(s) based at least in part upon the tags. The
enterprise application can then use the messages to perform further
business processing.

[0020] The actionable social data 112 is stored into a database in a
computer readable storage device 110. The computer readable storage
device 110 comprises any combination of hardware and software that allows
for ready access to the data that is located at the computer readable
storage device. For example, the computer readable storage device 110
could be implemented as computer memory operatively managed by an
operating system. The computer readable storage device 110 could also be
implemented as an electronic database system having storage on persistent
and/or non-persistent storage.

[0021]FIG. 2 shows an architecture for implementing a social media
marketing and engagement framework according to some embodiments of the
invention. Data from one or more social network systems are received into
the system. The social data may be either public social network sources
202 or private social network sources 204. Public social network
data/messages include, for example, publically available content from
public blog sites, twitter messages, RSS data, and social media sites
such as Facebook. Private social network data/messages include, for
example, content from internal company social networking sites.

[0022] In some embodiments, the data that is received for processing
includes non-social data. Such data includes, for example, enterprise
data from the non-public sources 204, such as, email, chats, transcribed
phone conversations, transcribed videos.

[0023] Semantic analysis is performed upon the received data. For example,
latent semantic analysis (LSA), an advanced form of statistical language
modeling, can be used to perform semantic analysis upon the social data.
This permits the system to understand the contextual and semantic
significance of terms that appear within the social data. This type of
analysis can be used, for example, to understand the difference between
the term "Galaxy" used for an astronomy contexts and "Galaxy" the name of
a professional soccer team.

[0024] Semantic filtering 208 is a mechanism that is provided to minimize
miss-categorizations of the social data. Much of the social data is
likely to contain content which is of very little interest to a business
organization. Semantic filtering is used to remove the irrelevant
material from the social data to reduce the occurrence of false
positives, false negatives, and inappropriate responses/rejections within
the actionable data. This permits the resulting data to be more relevant
and accurate when provided to the enterprise applications.

[0025] In some embodiments, all social data content is subject to semantic
filtering to reduce the excess "noise" of irrelevant data. In an
alternate embodiment, only public social network content undergoes
semantic filtering, such that the private social network content is not
subject to the semantic filtering. This embodiment is based on the
assumption that the public social network content is more likely to
contain data of little interest to the enterprise. In yet another
embodiment, both the public and private social network data are subject
to semantic filtering, but the filtering is handled differently so that
greater levels/intensity of filtering is imposed on the public data as
opposed to the private data.

[0026] The system performs semantic analysis and classification 212 to the
social media data. This permits the system to create and apply filters to
identify themes, and to cluster together like-minded messages, topics,
conversations, and content. There are numerous ways that can be taken to
semantically categorize the social network content. The categorizations
and classifications can be performed with an eye towards identifying, for
example: (a) customer insights, preferences, and intentions; (b)
demographic and social platform information; (c) industry and category
trends and emerging themes; and/or (d) customer/consumer viewpoints,
e.g., on price and product considerations, intent to switch services, and
customer satisfaction. Other categorizations and/or classifications for
performing auto-categorizations include, for example, items such as
"intent to switch", "customer satisfaction", "brand influencer", "passive
job candidate", "active job candidate", and "brand detractor".

[0027] Based upon the semantic analysis and classification, tags are
identified and associated with the social network content. The tags
correspond to areas of analytical importance with respect to the
organizations that will be consuming the results of the system. For
example, a business may seek to use the system to analyze social network
data to identify: (1) sales leads; (2) customer relations issues and
dissatisfied customers; (3) potential job candidates; and (4) HR topics.
If these are the business' goals, then at least some of the tags
associated with the classified social media content will, in some
embodiments, correspond to identification of the content that pertain to
these categories.

[0028] Social profile data 206 may also be accessed and associated with
the originator of specific items of social network content. This profile
data includes, for example, information about the social "importance" of
that person, e.g., using Klout data and/or follower count. The profile
data 206 may also include demographic information about the person,
including information about the person's income, age, profession, and
geographic location. This profile data is useful for many purposes. For
example, messages created by a person having a very high Klout score or
who has many followers may need to be placed onto a higher priority queue
for processing. As another example, the demographic information can be
used to direct a sales lead to the appropriate sales department, e.g., a
sales lead associated with a person in California may be directed to a
west coast sales representative while a sales lead from New York may be
directed to an east coast sales representative.

[0029] When analyzing internal social data, employee profile data may also
be accessed used as part of the analysis for the internal social network
content. When the person in an employee there is additional profile
information that may exist for the individual (e.g., organization
information--who is the manager/employee, job function, job level, peer
group, location, etc.). As noted above, external influence may be
analyzed, e.g., using a Klout score. Similarly, internal influence can
similarly be analyzed, tracked, and/or leveraged using various data
points, e.g., based at least in part on job role, responsibility, title,
number of employees managed by person, and/or number of individuals in
that person's organizational hierarchy.

[0030] The resulting set of data is the set of actionable social messages
that is stored in an actionable social message store 212. FIG. 3 shows a
table 302 of some example types of information that may be part of the
actionable social message. Column 308 identifies a title for the message,
e.g., based upon the "subject" or "topic" parameter associated with a
given social media message. Column 310 identifies an internet/web
link/address for the message. Column 312 identifies the specific source
of the message. Column 314 identifies the type of the message source.
Column 316 provides the date that is associated with the message. Column
318 provides some or all of the text associated with the message. Column
320 provides messaged quality data for the message, e.g., data regarding
the readability, subjectivity, and/or tonality of the message. Column 324
identifies the name/contact that is associated with the message. Column
326 identifies any additional relevant social profile data that may be
associated with the message and/or message originator.

[0031] Column 322 identifies the one or more tags that may be assigned to
the message. As described above, semantic analysis and classification may
be performed on the message to identify any tag(s) that should be
identified and associated with the social network content. For example,
row 304 corresponds to social message content that appears quite relevant
to the customer service of the organization being commented upon (e.g.,
commenting upon bad customer service from foo.com as described in Column
318). Therefore, this message will be associated with the appropriate tag
(e.g., "Customer Service") that corresponds to this area of importance to
the organization that will be consuming the results of the system (e.g.,
the organization being commented upon in the message as having bad
customer service).

[0032] As another example, row 306 corresponds to social message content
that discusses the desire to purchase a consumer item (e.g., a desire to
purchase a television from the foo.com organization as described in
Column 318). Therefore, this message will be associated with the
appropriate tag (e.g., "Sales Lead") that corresponds to this area of
importance to the organization (e.g., foo.com) that will be consuming the
results of the actionable message.

[0033] Returning back to FIG. 2, the actionable social messages are placed
into an actionable social message store 212. In some embodiments, this
actionable social message store 212 provides canonical storage for social
content that business vertical systems can access.

[0034] A user interface 214 is provided to permit users to view and act
upon the data within the actionable social message store. For example, a
Community Manager UX (user experience) can be provided as the user
interface 214. Using the Community Manager UX, a user can direct the flow
of messages to appropriate personnel to take responsibility for
performing pertinent actions with the messages in the actionable social
message store. For example, actionable social messages that are tagged as
a "Sales Lead" can be directed to sale personnel to contact the message
originator to make a sale. Similarly, actionable social messages that are
tagged as a "Customer Relations" can be directed to customer relations
personnel to contact the customer, e.g., to have retention department
personnel convince a dissatisfied customer avoid changing service
providers. Co-pending U.S. application Ser. No. 13/004,796, filed on Jan.
11, 2011, discloses an illustrative example of a Community Manager that
can be employed in some embodiments of the invention, which is hereby
incorporated by reference in its entirety.

[0035] The messages within the actionable social message store can be
processed using any suitable processing mechanism. In one embodiment,
manual processing is performed, whereby a user reviews the actionable
social messages and manually takes action to direct the message to the
appropriate destination. In an alternate embodiment, automated processing
is performed using a rules and workflow engine. In this approach, a set
of rules is provided in a rulebase, where the rules identify how the
messages should be handled and directed within the organization. The
rulebase can be constructed as a learning system, where feedback and a
neural network algorithm are used to improve the handling of messages
based upon looking at the results from past handling of messages.

[0036] The system then dispatches and/or creates the appropriate messages
to be sent to destinations within the enterprise. For example, tickets
can be sent to a social customer service tool 220, such as the RightNow
cloud-based customer service product available from Oracle Corporation.
As another example, the identity of possible employment candidates can be
sent to an HR department/HR application 222. In addition, opportunities
can be provided to a CRM system 224, where a record is automatically
created and/or process in the system for the sales lead. Product data and
comments can be provided to ecommerce tools 226, products, and groups,
e.g., to the ATG product available from Oracle Corporation. An analysis
tool/dashboard 218 (e.g., a business intelligence dashboard) may be
provided over the actionable social messages to provide visibility by
company decision makers using the analyzed data.

[0037] The messages to these vertical applications are dispatched based at
least in part upon the tags that are associated with the data in the
actionable social message store. When the other system takes action upon
receiving the messages, then in some embodiments an update is provided in
the actionable social message store with the update status of the
message.

[0038] The system shown in FIG. 2 therefore provides a framework for
integrating any part of a corporate infrastructure to handle social media
data. When any new social media source is identified, then the inventive
system can access and process that data like any of the other social data
already being accessed--transparent to the corporate infrastructure that
will eventually consume the results of that analysis. In addition, any
new components/applications to the corporate infrastructure can be easily
integrated, by configuring the rules within the Community Manager to
address workflow paths to that new component/application.

[0039] FIGS. 4A and 4B show flowcharts of approaches to implement some
embodiments of the invention. At 402, the social data is received. As
noted above, the social data may be received from any suitable source of
the data, including both public and private sources of social media data.

[0040] At 404, semantic filtering is performed upon the social data. The
filtering may be applied to some or all of the data. In some embodiments,
different levels of filtering may be applied to different types and/or
sources of data. For example, different levels of filtering may be
applied depending upon whether the social data is public social data or
private social data.

[0041] At 406, semantic analysis and classification is performed on the
social media data. Based upon the results of the semantic analysis and
classification, tags are identified and are associated with the
messages/content, at 408. Thereafter, at 410, the actionable social
messages are stored into an actionable social message store.

[0042] At 412, the data within the actionable social message store is
retrieved for processing. The message tag is reviewed at 408. At 414,
identification is made of the appropriate action to take with regards to
the message.

[0043] Different approaches can be taken to process the messages in the
actionable social message store. In the approach of 416a, manual
processing is performed such that a user reviews the actionable social
messages and manually takes action to direct the message to the
appropriate destination. In the approach of 416b, automated processing is
performed using a rules and workflow engine, where a set of rules is
provided in a rulebase. The rules identify how the messages should be
handled and directed within the organization.

[0044] Thereafter, at 418, the appropriate action is taken with respect to
the message. For example, tickets can be sent to a social customer
service cloud product, the identity of possible employment candidates can
be sent to an HR department, opportunities can be provided to a CRM
system, and product data/comments can be provided to ecommerce products
and groups.

[0045] Another action that can performed is to assign action items and due
dates based upon this data to key leaders in the organization (e.g.,
assign an action to marketing lead for internal communication strategy on
key topics, assign action to HR lead for improving a key EE program,
assign action to engineering to improve key metric, assign action to
sales to share top number of wins and losses with key leaders, assigning
an action to a recruiter).

Illustrative Example for Internal Social Network Content

[0046] The present invention can be applied to analyze and act upon any
type of social data from any source of the data. In particular,
embodiments of the invention can be applied to analyze and act upon both
public and private sources of social media data. This portion of the
disclosure describes an illustrative embodiment where the invention is
applied to internal social network content.

[0047] As noted above, the present invention provides a mechanism to
analyze and act upon data from social networks. This provides numerous
advantages for an organization, since social networks have now become
commonplace experiences for many individual that use the internet where
as a routine matter of their daily activities on the internet, many users
will regularly access and use public social networks to post content,
convey thoughts, and engage in conversations.

[0048] For many enterprises and businesses, it would be very useful to be
able leverage the capabilities of social networks to improve the way that
the enterprises and businesses are run. However, given that social
network is a relatively new phenomenon, conventionally most organizations
have either ignored it or focused on the following aspects of social
network:

[0049] 1. Policies to prevent usage of social network by
employees, except those involved with the organization own external
social presence

[0050] 2. Policies to improve customer relationship
through the organization's social presence

[0051] 3. Policies that allow
use of social network, but prevent posting of anything work related

[0052] Recently, organizations are starting to realize the value of an
internal social network as a tool to improve collaboration within the
organization. The internal social networks can be configured to provide
an effective mechanism to allow users of the system to interact and
collaborate with each other. For example, consider a CRM application,
which is designed to facilitate the ability of a business to create,
develop, and build relationships with its customers or potential
customers--with the obvious intent to obtain or increases the business'
sales to the customers. In this type of system, it would be very
desirable to allow the internal users of an organization to use the
internal social network to stay informed and collaborate for related
business activities and customers/leads. An example internal social
network is the Oracle Social Network product, available from Oracle
Corporation of Redwood Shores, Calif.

[0053] The problem is that while some company executives may understand
the value of internal social networks, others may not necessarily see its
value or understand its benefits to the organization.

[0054] The present embodiment provides a method, system, and computer
program product for semantically analyzing the content within an internal
social network. Using the results of the analysis, the executives can
gain a better understanding of, and insight into, the organization and
its employees. A dashboard tool may be used in some embodiments of the
invention to visualize the results of the semantic analysis.

The invention provides numerous benefits and advantages. The invention
provides an effective framework and execution path for organizations to
enable, encourage, or monitor their internal social networks. In
addition, the invention permits executives of the organization to
directly derive value from a thriving internal social network. These
benefits and values will help fund and grow the internal social networks
as not only a collaboration tool, but also as a tool to align the
organization.

[0055]FIG. 5 illustrates an example system 502 which may be employed in
some embodiments of the invention to implement analysis of an internal
social network. The system 502 includes one or more users at one or more
user stations 102.

[0056] The data 504 operated upon by system 502 is content from an
internal social network. The internal social network provides mechanisms
and tools to permit members of an organization to interact and
collaborate with each other. An example internal social network is the
Oracle Social Network product, available from Oracle Corporation of
Redwood Shores, Calif. The Oracle Social Network product provides a
method by which users can create a `conversation` that is associated with
a business object. Users who are collaborating on the business object
will document their discussion in the conversation. One example is
discussion about new product design in the public cloud space. More
details regarding an approach to implement an internal social network is
described in co-pending U.S. application Ser. No. 13/622,071, filed on
Sep. 20, 2012, entitled "Social Network System with Social Objects" and
U.S. application Ser. No. 13/888,888, filed on May 7, 2013, entitled
"Method and System for Integrating and Enterprise Application with a
Social Networking Application", which are hereby incorporated by
reference in their entirety.

[0057] The present embodiment is illustratively explained with reference
to the Oracle Social Network. It is noted, however, that the invention is
applicable to any internal social network, and indeed, has wide
applicability in general to many types of organizational data. For
example, internal company email and external social traffic may be
semantically analyzed in similar ways to provide benefits to the
organization. Therefore, the invention is not to be limited in its
application to just to the Oracle Social Network.

[0058] A semantic analysis tool 108 is used to analyze the internal social
network data, e.g., as described above with respect to FIGS. 1-4.
Semantic analysis and filtering is performed upon the internal social
network data to generate analysis results 510, which are stored into a
database in a computer readable storage device 504.

[0059] Any suitable type of semantic analysis can be performed upon the
internal social network data. For example, latent semantic analysis
(LSA), an advanced form of statistical language modeling, can be used to
perform semantic analysis upon the social data. This permits the system
to understand the contextual and semantic significance of terms that
appear within the social data. As a simple example semantic analysis can
be used to understand the difference between the terms "Boss" when used
to refer to a manager at work, when the "Boss" term is used to refer to a
line of men's clothing, or where the term "The Boss" is used as the
nickname for a certain well-known singer.

[0060] The system performs semantic analysis and classification to the
internal social network data. This permits the system to create and apply
filters to identify themes, and to cluster together like-minded messages,
topics, conversations, and content for those themes. For example, the
internal social network data can be analyzed to identify themes such as
"compensation", "company performance and results", "company stock
process", "acquisitions", "management policies", etc.

[0061] Therefore, there are numerous ways that can be taken to
semantically categorize the internal social network content. The
categorizations and classifications can be performed with an eye towards
identifying themes and categories that would be of interest to the
organization and its executives, with a particular focus on employee
insights, preferences, and intentions and thoughts on industry, and
category trends and emerging themes.

[0062] The analysis results can be used to identify topics that are
considered to be of most importance to the employees, e.g., to identify
the most popular topics on the internal social network. Furthermore, the
invention can be used to determine whether there is a positive or
negative sentiment about those topics. "Sentiment" refers to an opinion
or feeling associated with a given item or type of content.

[0063] As noted above, employee profile data may also be accessed used as
part of the analysis for the internal social network content. The profile
data may also include, for example, demographic information about the
originator of internal social network content, including information
about the person's department, position, title, income, years of
experience at the company, and geographic location. This profile data is
useful for many purposes. For example, the profile data can be used to
slice the analysis results by department, location, or other attributes,
e.g., to understand better if certain sentiment is local or global.

[0064] Semantic filtering is a mechanism that is provided to minimize
miss-categorizations of the social data. Semantic filtering is used to
remove the irrelevant material from the social data to reduce the
occurrence of false positives, false negatives, and inappropriate
responses/rejections within the actionable data. This permits the
resulting data to be more relevant and accurate when provided to the
company executives.

[0065] In some embodiments, none of the internal social data is subject to
semantic filtering. This embodiment is based on the assumption that the
bulk of the internal social network content is likely to contain relevant
data (in contrast to data from public social networks which likely
contain an overwhelming quantity of irrelevant data). In an alternate
embodiment, the internal social network data is subject to semantic
filtering, particularly if past analysis shows the significant presence
or quantity of irrelevant content among the raw internal social network
data. In yet another embodiment, if the system is used to analyze both
public and internal social network data, then either, or both the public
and private social network data are subject to semantic filtering, but
the filtering can be handled differently, e.g., so that greater
levels/intensity of filtering are imposed on the public data as opposed
to the internal data.

[0066] The analysis results 510 can be embodied as actionable social data
in an actionable social message store, where tagging is implemented to
tag the messages with appropriate tags. The tags correspond to areas of
analytical importance with respect to the individuals or departments
within an organization that will consume the results of the analysis.
Therefore, the semantic analysis and classification activities will
identify and associate tags to content from the internal social network
as necessary to make sure the "actionable" content is appropriately
handled by downstream reviewers.

[0067] The set of analysis results 510 can also be accessed and visually
reviewed by individual and organizations within a company. In some
embodiments, a dashboard tool 508 is used to access the analysis results.
A "dashboard" is a user interface mechanism that is often used to provide
views of key metrics and indicators relevant to a particular objective or
business process. Therefore, dashboards typically use graphs, charts, and
other visual objects to show summaries, trends, and comparisons of data.
The dashboard can be configured to show analysis results of the internal
social data that are needed to monitor the health and opportunities of
the business, including for example, data that focus on high level
measures of performance and forecasts. For analysis purposes, the
dashboards often include more in the way of context data, comparisons,
and history for the internal social data. The dashboards may also be used
to monitor events and activities that are changing more often. The
dashboards also may also support drilling down into the underlying
details of the high level data. A suitable product that can be used in
embodiments of the invention to provide dashboards is the Oracle BI
(Business Intelligence) product, available from Oracle Corporation of
Redwood Shores, Calif. In addition, set of analysis results 510 can also
be automatically analyzed and processed using the workflow engine
described above.

[0068] In some embodiments, the analysis results can be integrated with an
enterprise application used by the organization. The enterprise
application comprises any business-related application that provides
visibility and control over various aspects of a business. Such
enterprise/business applications can include, without limitation, Human
resources ("HR") applications, customer relations management ("CRM")
applications, enterprise resource planning ("ERP") applications, supply
chain management applications, and other applications dealing with
various finance, accounting, manufacturing, human resources, and/or
distribution functions, to name but a few examples. For example,
particular themes, sentiment, and content of importance to the human
resources department may be sent as messages to be consumed by a HR
computing system or department.

[0069] In one embodiment, tagged messages can be used to send the
actionable content to the appropriate destination. For example,
actionable social messages that are tagged as a "HR" can be directed to
the human resources department to be handled by appropriate personnel to
handle possible employee/employment issues. A user interface (e.g.,
Community Manager UX) can be provided to permit users to view and act
upon the actionable social content. Using the Community Manager UX, a
user can direct the flow of messages to appropriate personnel to take
responsibility for performing pertinent actions with the messages in the
actionable social message store.

[0070] The company management can take appropriate courses of action to
address the identified content/topics from the internal social network.
For example, the company executives can tailor communications to the
employees (such as scheduling an All Hands meeting) to address the topics
the employee base (e.g., department/location) considers important (e.g.,
most popular topics). As noted above, the identified content/topic can be
identified from the dashboard. The identified content/topic can also be
identified using the Community Manager UX and tags that are associated
with the content.

[0071] As another example, the company management can use the analysis
results to encourage and fuel momentum behind key projects. This is
accomplished, for example, by finding where it has already taken hold and
empowering those groups.

[0072] As yet another example, the invention can be used to make budget
allocations, spot bonuses, or make other monetary decisions. This can be
used to encourage momentum behind the topics that the executives
considers important.

[0073] In addition, the organization and its management can look into
negative sentiments around important initiatives. This is useful, for
example, to figure out how to address those negatives sentiments and
topics (e.g., by communication, restructuring, etc.).

[0074]FIG. 6 shows a flowchart of an approach to implement some
embodiments of the invention. At 602, the internal social data is
received by the analysis system. As noted above, the internal social data
may be received from any suitable internal social network. In some
embodiments, the analysis can be performed based upon a combination of
data from internal social networks as well as other sources of data,
including public sources of social media data and email content.

[0075] At 604, semantic analysis and classification is performed on the
social media data. This action is performed on a regular basis, e.g., to
identify the most popular and most important topics discussed in the
content within the internal social networks. Semantic filtering may also
be applied to some or all of the social network data.

[0076] At 606, the analysis results are used to populate a dashboard with
periodic statistics on the most popular and most important topics. The
"importance" of topics sufficient to be displayed on the dashboard can be
selected, for example, by the company executives. Using the dashboard,
the executive can gain insight into: (a) What the employees consider most
important (the most popular topics); (b) If there is positive and/or
negative sentiment about the most important topics; and (c) Slice this
data by department, location, or other attributes to understand better if
a sentiment is local or global.

[0077] At 608, the company executives can then take actions appropriate to
address the topics. Such actions include, for example: (a) Tailor
communication to address the topics the employee base considers
important; (b) Encourage and fuel momentum behind key projects by finding
where it has already taken hold and empowering those groups; (c) Making
budget allocations, spot bonuses, or other monetary decisions that
encourage momentum behind the topics that the exec considers important;
(d) Look into negative sentiments around important initiatives, and
figure out how to address those (communication, restructuring, etc.).

[0078] Therefore, what has been described is an approach for implementing
a system, method, and computer program product for allowing a business
entity to access and integrate with social media data. The invention is
capable of accessing data across multiple types of internet-based sources
of social data and commentary and to perform semantic analysis upon that
data.

[0080] According to one embodiment of the invention, computer system 1400
performs specific operations by processor 1407 executing one or more
sequences of one or more instructions contained in system memory 1408.
Such instructions may be read into system memory 1408 from another
computer readable/usable medium, such as static storage device 1409 or
disk drive 1410. In alternative embodiments, hard-wired circuitry may be
used in place of or in combination with software instructions to
implement the invention. Thus, embodiments of the invention are not
limited to any specific combination of hardware circuitry and/or
software. In one embodiment, the term "logic" shall mean any combination
of software or hardware that is used to implement all or part of the
invention.

[0081] The term "computer readable medium" or "computer usable medium" as
used herein refers to any medium that participates in providing
instructions to processor 1407 for execution. Such a medium may take many
forms, including but not limited to, non-volatile media and volatile
media. Non-volatile media includes, for example, optical or magnetic
disks, such as disk drive 1410. Volatile media includes dynamic memory,
such as system memory 1408.

[0082] Common forms of computer readable media includes, for example,
floppy disk, flexible disk, hard disk, magnetic tape, any other magnetic
medium, CD-ROM, any other optical medium, punch cards, paper tape, any
other physical medium with patterns of holes, RAM, PROM, EPROM,
FLASH-EPROM, any other memory chip or cartridge, cloud-based storage, or
any other medium from which a computer can read.

[0083] In an embodiment of the invention, execution of the sequences of
instructions to practice the invention is performed by a single computer
system 1400. According to other embodiments of the invention, two or more
computer systems 1400 coupled by communication link 1415 (e.g., LAN,
PTSN, or wireless network) may perform the sequence of instructions
required to practice the invention in coordination with one another.

[0084] Computer system 1400 may transmit and receive messages, data, and
instructions, including program, i.e., application code, through
communication link 1415 and communication interface 1414. Received
program code may be executed by processor 1407 as it is received, and/or
stored in disk drive 1410, or other non-volatile storage for later
execution.

[0085] In the foregoing specification, the invention has been described
with reference to specific embodiments thereof. It will, however, be
evident that various modifications and changes may be made thereto
without departing from the broader spirit and scope of the invention. For
example, the above-described process flows are described with reference
to a particular ordering of process actions. However, the ordering of
many of the described process actions may be changed without affecting
the scope or operation of the invention. The specification and drawings
are, accordingly, to be regarded in an illustrative rather than
restrictive sense.